About Case Study

Account takeover (ATO) attacks represent a significant threat often leading to severe financial and reputational consequences for both individuals and organizations. In an ATO attack, unauthorized individuals gain control over user accounts, which can result in the theft of sensitive information, unauthorized financial transactions, and further exploitation of compromised identities. In this case study, discover how a leading fintech leveraged FOCAL Device Intelligence to proactively detect and block ATO attacks, reducing fraud by 90% while maintaining a seamless user experience.

Techniques Used in Account Takeover by Fraudsters

We will uncover the sophisticated methods employed in account takeover (ATO) attacks, including phishing, credential stuffing, and malware, and explore their impact on individuals and businesses.

Patterns of Attack & Fraud Sophistication

Dive into key patterns like single device-multiple accounts, cluster-based attacks, and chained device activities, highlighting the evolving strategies used by fraudsters to evade detection.

Impact of Account Takeovers on Businesses

Account takeover attacks lead to significant consequences for businesses, including financial losses, regulatory and legal penalties, and reputational damage that erodes customer trust.

How FOCAL Stopped ATO & Cut Fraud by 90%

Discover how I-driven Device Intelligence empowered this fintech to block compromised devices and neutralize real-time threats to protect their business and customers.

Case Study Insights

Examine a real-world case study of a fintech platform’s defense against a multi-layered ATO attack, showcasing the effectiveness of AI in thwarting sophisticated fraud schemes

How Does It Help the Client’s Organization?

1. Account Takeover

Track unauthorized access to user accounts by identifying suspicious device behavior and recognizing previously used devices. By detecting anomalies in device attributes, Device Fingerprinting stops fraudsters from taking over accounts without impacting genuine user experience.

2. Multi-Account Abuse

Detect users from bypassing limits on accounts by identifying devices trying to create or access multiple accounts, enabling you to maintain platform integrity and prevent abuse with precise device tracking.

3. Promo and Coupon Abuse

Identify users attempting to exploit sign-up bonuses or promotional offers by identifying repeat devices even when accounts use different credentials, ensuring fair usage and protecting your marketing spend.

4. VPN Detection

Automatically collect IP address for each session and detect VPN usage across multiple sessions to flag potential location spoofing attempts

5. Prevent Account Sharing

Uncover unauthorized account sharing by monitoring device usage patterns linked to an account, identifying multiple devices or sessions indicating shared access.

6. Device Spoofing

Discover device spoofing by leveraging unique hardware attributes that are hard to fake, ensuring reliable identification and flagging of fraudulent devices across sessions.

7. Jailbroken Devices

Identify devices with compromised security (rooted/jailbroken) to mitigate risks. FOCAL device fingerprinting flags jailbroken devices in real time, enabling proactive fraud prevention and enhanced security.

8. Device Emulation

Detects fraudsters using emulated environments to bypass anti-fraud measures and automate fraud attempts. Identify artificial device attributes and synthetic interactions, flagging devices running on emulation software or virtual machines.